title: Bayesian approach to estimate AUC, partition coefficient and drug targeting index for studies with serial sacrifice design.
abstract: OBJECTIVE
  The current study presents a Bayesian approach to non-compartmental analysis (NCA), which provides the accurate and precise estimate of AUC 0 (∞) and any AUC 0 (∞) -based NCA parameter or derivation.
  
  
  METHODS
  In order to assess the performance of the proposed method, 1,000 simulated datasets were generated in different scenarios. A Bayesian method was used to estimate the tissue and plasma AUC 0 (∞) s and the tissue-to-plasma AUC 0 (∞) ratio. The posterior medians and the coverage of 95% credible intervals for the true parameter values were examined. The method was applied to laboratory data from a mice brain distribution study with serial sacrifice design for illustration.
  
  
  RESULTS
  Bayesian NCA approach is accurate and precise in point estimation of the AUC 0 (∞) and the partition coefficient under a serial sacrifice design. It also provides a consistently good variance estimate, even considering the variability of the data and the physiological structure of the pharmacokinetic model. The application in the case study obtained a physiologically reasonable posterior distribution of AUC, with a posterior median close to the value estimated by classic Bailer-type methods.
  
  
  CONCLUSIONS
  This Bayesian NCA approach for sparse data analysis provides statistical inference on the variability of AUC 0 (∞) -based parameters such as partition coefficient and drug targeting index, so that the comparison of these parameters following destructive sampling becomes statistically feasible.
authors.affiliation: Department of Pharmaceutics, University of Minnesota, 308 Harvard St. SE, Minneapolis, Minnesota, 55455, USA.
authors.forename: Tianli
authors.initials: T
authors.lastname: Wang
authors.forename: Kyle
authors.initials: K
authors.lastname: Baron
authors.forename: Wei
authors.initials: W
authors.lastname: Zhong
authors.forename: Richard
authors.initials: R
authors.lastname: Brundage
authors.forename: William
authors.initials: W
authors.lastname: Elmquist
mesh_terms: D000818:Animals; D000970:Antineoplastic Agents / Q000008:administration & dosage / Q000097:blood* / Q000493:pharmacokinetics*; D019540:Area Under Curve; D001499:Bayes Theorem; D001921:Brain / Q000378:metabolism; D016503:Drug Delivery Systems; D051379:Mice; D008954:Models, Biological; D011799:Quinazolines / Q000008:administration & dosage / Q000097:blood* / Q000493:pharmacokinetics*; D007211:Indoles
chemical_list: D000970:Antineoplastic Agents; D011799:Quinazolines; C500926:cediranib; D007211:Indoles